Gitler AD, Dhillon P, Shorter J (2017) Neurodegenerative disease: models, mechanisms, and a new hope. Dis Models Mech 10:499–502

Article  CAS  Google Scholar 

Botas A, Campbell HM, Han X, Maletic-Savatic M Metabolomics of neurodegenerative diseases. in Int Rev Neurobiol 122 53–80 (Elsevier, 2015).

Nichols E et al (2022) Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the global burden of Disease Study 2019. Lancet Public Health 7:e105–e125

Article  Google Scholar 

Nandi A et al (2022) Global and regional projections of the economic burden of Alzheimer’s disease and related dementias from 2019 to 2050: a value of statistical life approach. eClinicalMedicine 51:101580

Article  PubMed  PubMed Central  Google Scholar 

Liguori C et al (2022) Biomarkers of cerebral glucose metabolism and neurodegeneration in Parkinson’s Disease: a cerebrospinal fluid-based study. JPD 12:537–544

Article  CAS  PubMed  Google Scholar 

Öhman A, Forsgren L (2015) NMR metabonomics of cerebrospinal fluid distinguishes between Parkinson’s disease and controls. Neurosci Lett 594:36–39

Article  PubMed  Google Scholar 

Albanese M et al (2016) Cerebrospinal fluid lactate is associated with multiple sclerosis disease progression. J Neuroinflammation 13:36

Article  PubMed  PubMed Central  Google Scholar 

Arlt S et al (2012) Homocysteine Metabolism, and cerebrospinal fluid markers for Alzheimer’s Disease. JAD 31:751–758Dimethylarginines

Jahn T et al (2021) Cholesterol metabolites and plant sterols in cerebrospinal fluid are associated with Alzheimer’s cerebral pathology and clinical disease progression. J Steroid Biochem Mol Biol 205:105785

Article  CAS  PubMed  Google Scholar 

Abdel-Khalik J et al (2017) Defective cholesterol metabolism in amyotrophic lateral sclerosis. J Lipid Res 58:267–278

Article  CAS  PubMed  Google Scholar 

Gray E et al (2015) The longitudinal cerebrospinal fluid metabolomic profile of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degeneration 16:456–463

Article  CAS  Google Scholar 

Emdin CA, Khera AV, Kathiresan S (2017) Mendelian Randomization JAMA 318:1925

Lawlor DA, Harbord RM, Sterne JAC, Timpson N (2008) Davey Smith, G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27:1133–1163

Article  PubMed  Google Scholar 

Davey Smith G, Hemani G (2014) Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 23:R89–R98

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sheehan NA, Didelez V, Burton PR, Tobin MD (2008) Mendelian randomisation and causal inference in Observational Epidemiology. PLoS Med 5:e177

Article  PubMed  PubMed Central  Google Scholar 

Panyard DJ et al (2021) Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations. Commun Biol 4:63

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bellenguez C et al (2022) New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat Genet 54:412–436

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jiang L, Zheng Z, Fang H, Yang J (2021) A generalized linear mixed model association tool for biobank-scale data. Nat Genet 53:1616–1621

Article  CAS  PubMed  Google Scholar 

Van Rheenen W et al (2021) Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology. Nat Genet 53:1636–1648

Article  PubMed  PubMed Central  Google Scholar 

International Multiple Sclerosis Genetics Consortium (2019) Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365

Schwartzentruber J et al (2021) Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes. Nat Genet 53:392–402

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sakaue S et al (2021) A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 53:1415–1424

Article  CAS  PubMed  Google Scholar 

Nalls MA et al (2019) Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol 18:1091–1102

Article  CAS  PubMed  PubMed Central  Google Scholar 

Glanville KP, Coleman JRI, O’Reilly PF, Galloway J, Lewis CM (2021) Investigating Pleiotropy between Depression and Autoimmune diseases using the UK Biobank. Biol Psychiatry Glob Open Sci 1:48–58

Article  PubMed  PubMed Central  Google Scholar 

Pierce BL, Ahsan H, VanderWeele TJ (2011) Power and instrument strength requirements for mendelian randomization studies using multiple genetic variants. Int J Epidemiol 40:740–752

Article  PubMed  Google Scholar 

Verbanck M, Chen C-Y, Neale B, Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases. Nat Genet 50:693–698

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hemani G et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. eLife 7:e34408

Article  PubMed  PubMed Central  Google Scholar 

Orts-Del’Immagine A, Wyart C (2017) Cerebrospinal-fluid-contacting neurons. Curr Biol 27:R1198–R1200

Article  PubMed  Google Scholar 

Wakamatsu K et al (2022) Metabolites and biomarker compounds of neurodegenerative diseases in Cerebrospinal Fluid. Metabolites 12:343

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bakker L et al (2023) Correlations between kynurenines in plasma and CSF, and their relation to markers of Alzheimer’s disease pathology. Brain Behav Immun 111:312–319

Article  CAS  PubMed  Google Scholar 

Dong R et al (2023) CSF metabolites associated with biomarkers of Alzheimer’s disease pathology. Front Aging Neurosci 15:1214932

Article  CAS  PubMed  PubMed Central  Google Scholar 

Giil LM et al (2017) Kynurenine Pathway metabolites in Alzheimer’s Disease. JAD 60:495–504

Article  CAS  PubMed  Google Scholar 

Heylen A et al (2023) Brain kynurenine pathway metabolite levels may reflect extent of Neuroinflammation in ALS, FTD and early onset AD. Pharmaceuticals 16:615

Article  CAS  PubMed  PubMed Central  Google Scholar 

Xue C et al (2023) Tryptophan metabolism in health and disease. Cell Metabol 35:1304–1326

Article  CAS  Google Scholar 

Fernandes BS, Inam ME, Enduru N, Quevedo J, Zhao Z (2023) The kynurenine pathway in Alzheimer’s disease: a meta-analysis of central and peripheral levels. Brazilian J Psychiatry. https://doi.org/10.47626/1516-4446-2022-2962

Article  Google Scholar 

Maddison DC, Giorgini F (2015) The kynurenine pathway and neurodegenerative disease. Semin Cell Dev Biol 40:134–141

Article